How do you evaluate an agent's trajectory and tool-use accuracy, not just its final answer?

Final-answer accuracy hides how an agent got there. The signal is trajectory-level metrics: tool-selection and argument accuracy, step efficiency, and matching against reference paths, plus when exact-match is the wrong yardstick.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Agent Engineering·The single-agent loopFree14mStopping is a design decision, not a budgetBudgets stop a runaway agent. They do not tell it when it is finished, and the difference between those two things is where most agent quality is lost. This lesson covers the four ways to define done and what to return when you never got there.Applied AI Engineering·EvaluationSign in14mLetting a model grade the work, and how it lies to youUsing a model to score output is the only way to evaluate open-ended work at any scale, and it has five documented biases that will flatter you if you do not design around them. This lesson covers each one and the cheap defence.

UP NEXT ON YOUR JOURNEY

Next in this trackWhy do agents fail on long-horizon tasks, and how do you keep reliability up over many steps?Next in this trackWhat are Reflexion and self-critique loops, and when do they actually improve an agent?Next in this trackHow do you design human-in-the-loop checkpoints so an agent can pause, ask, and resume?

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